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                    Introduction
            
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                    Android
            
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                    多task的应用
            
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                    Task和回退栈
            
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                    一个由Proguard与FastJson引起的血案
            
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                    Computer Vision
            
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                    计算机视觉常用工具
            
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                    Machine Learning
            
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                    技巧
            
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                    FaceBook: 1 hour training ImageNet
            
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                    实践
            
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                    Pytorch实验代码的亿些小细节
            
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                    工具
            
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                    Tensorflow学习笔记
            
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                    MXNet踩坑手记
            
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                    Matlab
            
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                    Matlab Remote IPC自动化数据处理
            
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                    Papers
            
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                    Classification
            
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                    Re-identification
            
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                    CVPR2018:TFusion完全解读
            
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                    ECCV2018:TAUDL
            
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                    CVPR2018:Graph+reid
            
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                    Person Re-identification
            
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                    CVPR2016 Re-id
            
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                    Camera topology and Person Re-id
            
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                    Deep transfer learning Person Re-id
            
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                    Evaluate
            
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                    Object Detection
            
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                    读论文系列·干货满满的RCNN
            
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                    读论文系列·SPP-net
            
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                    读论文系列·Fast RCNN
            
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                    读论文系列·Faster RCNN
            
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                    读论文系列·YOLO
            
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                    读论文系列·SSD
            
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                    读论文系列·YOLOv2 & YOLOv3
            
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                    读论文系列·detection其他文章推荐
            
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                    Depth
            
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                    3D vision
            
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                    数据集相关
            
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                    光流相关
            
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                    Hashing
            
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                    CVPR2018: SSAH
            
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                    大杂烩
            
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                    CNCC2017 琐记
            
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                    ECCV 2016 Hydra CCNN
            
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        <li class="chapter " data-level="1.4.4.7.3" data-path="../overview/cncc_cv.html">
            
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                    CNCC2017深度学习与跨媒体智能
            
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                    MLA2016笔记
            
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        <li class="chapter " data-level="1.4.5" data-path="../../zzh_ml_notes/">
            
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                    《机器学习》（周志华）读书笔记
            
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                    西瓜书概念整理
            
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                    绪论
            
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                    模型评估与选择
            
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                    线性模型
            
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        <li class="chapter " data-level="1.4.5.1.4" data-path="../../zzh_ml_notes/melon/ch04.html">
            
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                    决策树
            
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                    神经网络
            
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        <li class="chapter " data-level="1.4.5.1.6" data-path="../../zzh_ml_notes/melon/ch06.html">
            
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                    支持向量机
            
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        <li class="chapter " data-level="1.4.5.1.7" data-path="../../zzh_ml_notes/melon/ch07.html">
            
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                    贝叶斯分类器
            
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        <li class="chapter " data-level="1.4.5.1.8" data-path="../../zzh_ml_notes/melon/ch08.html">
            
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                    集成学习
            
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        <li class="chapter " data-level="1.4.5.1.9" data-path="../../zzh_ml_notes/melon/ch09.html">
            
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                    聚类
            
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        <li class="chapter " data-level="1.4.5.1.10" data-path="../../zzh_ml_notes/melon/ch10.html">
            
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                    降维与度量学习
            
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        <li class="chapter " data-level="1.4.5.1.11" data-path="../../zzh_ml_notes/melon/ch11.html">
            
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                    特征选择与稀疏学习
            
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        <li class="chapter " data-level="1.4.5.1.12" data-path="../../zzh_ml_notes/melon/ch12.html">
            
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                    计算学习理论
            
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        <li class="chapter " data-level="1.4.5.1.13" data-path="../../zzh_ml_notes/melon/ch13.html">
            
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                    半监督学习
            
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        <li class="chapter " data-level="1.4.5.1.14" data-path="../../zzh_ml_notes/melon/ch14.html">
            
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                    概率图模型
            
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                                <h1 id="&#x5355;&#x76EE;&#x6DF1;&#x5EA6;">&#x5355;&#x76EE;&#x6DF1;&#x5EA6;</h1>
<p>&#x5355;&#x76EE;&#x6DF1;&#x5EA6;&#x53EF;&#x4EE5;&#x5206;&#x4E3A;&#x4E24;&#x4E2A;&#x5C0F;&#x9886;&#x57DF;&#xFF0C;&#x4E00;&#x4E2A;&#x662F;Mono depth estimation&#xFF0C;&#x662F;&#x771F;&#x5355;&#x76EE;&#xFF0C;&#x4ECE;&#x56FE;&#x50CF;&#x8BED;&#x4E49;&#x51FA;depth&#xFF0C;&#x8F93;&#x51FA;&#x7684;depth scale&#x901A;&#x5E38;&#x548C;&#x771F;&#x5B9E;&#x4E16;&#x754C;&#x662F;&#x4E0D;&#x5BF9;&#x9F50;&#x7684;&#xFF0C;&#x53E6;&#x4E00;&#x4E2A;&#x662F;Multi view stereo&#xFF0C;&#x5229;&#x7528;&#x524D;&#x540E;&#x5E27;&#x56FE;&#x50CF;&#x548C;pose&#xFF08;pose&#x4E5F;&#x53EF;&#x80FD;&#x771F;&#x5B9E;&#x6A21;&#x578B;&#x4F30;&#x51FA;&#x6765;&#x7684;&#xFF09;&#xFF0C;&#x901A;&#x8FC7;&#x5BF9;&#x6781;&#x51E0;&#x4F55;&#x51FA;&#x6DF1;&#x5EA6;&#x3002;</p>
<h2 id="mono-depth-estimation">Mono Depth Estimation</h2>
<ul>
<li>Before 2020: &#x53EF;&#x4EE5;&#x53C2;&#x8003;&#x77E5;&#x4E4E;&#x4E0A;&#x8FD9;&#x7BC7;&#x7EFC;&#x8FF0;&#xFF0C;&#x633A;&#x5B8C;&#x6574;&#x7684;&#xFF1A;<a href="https://zhuanlan.zhihu.com/p/111759578" target="_blank">https://zhuanlan.zhihu.com/p/111759578</a></li>
<li>Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields<a href="https://github.com/dulucas/Displacement_Field" target="_blank">[code(pytorch)]</a><a href="https://arxiv.org/abs/2002.12730" target="_blank">[paper]</a></li>
</ul>
<blockquote>
<p>CVPR2020,&#x901A;&#x8FC7;&#x8BA1;&#x7B97;displacement&#xFF0C;&#x5BF9;depth&#x505A;refine&#xFF0C;&#x6240;&#x8C13;displacement&#xFF0C;&#x5373;&#x5C06;&#x5468;&#x56F4;&#x4E00;&#x5B9A;&#x8303;&#x56F4;&#x5185;&#x7684;&#x67D0;&#x4E2A;&#x50CF;&#x7D20;&#x7684;depth&#x632A;&#x8FC7;&#x6765;&#x4F5C;&#x4E3A;&#x81EA;&#x5DF1;&#x7684;depth&#xFF0C;&#x7F51;&#x7EDC;&#x4E3A;&#x6BCF;&#x4E2A;&#x50CF;&#x7D20;&#x8F93;&#x51FA;&#x8981;&#x632A;&#x7684;&#x76EE;&#x6807;&#x50CF;&#x7D20;&#x5750;&#x6807;&#xFF0C;&#x4ECE;&#x800C;&#x524A;&#x5F31;CNN depth&#x7684;&#x8FB9;&#x7F18;&#x6A21;&#x7CCA;&#x73B0;&#x8C61;&#x3002;</p>
</blockquote>
<ul>
<li>Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume</li>
</ul>
<blockquote>
<p>CVPR2020, &#x5C06;&#x56DE;&#x5F52;&#x7684;&#x76EE;&#x6807;&#x4ECE;inverse depth&#x6362;&#x6210;&#x4E86;disparity&#xFF0C;&#x7528;softmax probability&#x4E58;disp&#x6C42;&#x548C;&#x7684;&#x65B9;&#x5F0F;&#x56DE;&#x5F52;disparity&#xFF0C;&#x540C;&#x65F6;&#x52A0;&#x4E86;&#x4E00;&#x5757;attention&#x6A21;&#x5757;&#x5BF9;disparity&#x505A;&#x4FEE;&#x6B63;&#xFF0C;&#x548C;&#x5176;&#x4ED6;&#x5355;&#x76EE;&#x6587;&#x7AE0;&#x7C7B;&#x4F3C;&#xFF0C;&#x591A;&#x6B21;decode&#x5F97;&#x5230;&#x66F4;&#x5927;&#x5C3A;&#x5BF8;&#x7684;disparity&#x3002;&#x8FD9;&#x7BC7;&#x8BBA;&#x6587;&#x4E5F;&#x662F;&#x505A;&#x81EA;&#x76D1;&#x7763;&#xFF0C;&#x6587;&#x7AE0;&#x4E2D;&#x63D0;&#x5230;&#x4E00;&#x4E2A;&#x64CD;&#x4F5C;&#x6BD4;&#x8F83;&#x5B9E;&#x7528;&#xFF1A;&#x8BA1;&#x7B97;warp loss&#x65F6;&#xFF0C;&#x5982;&#x679C;warp&#x7684;&#x53F3;&#x56FE;&#x7684;cost&#x6BD4;&#x539F;&#x59CB;&#x53F3;&#x56FE;&#x8FD8;&#x5927;&#xFF0C;&#x5C31;&#x8BA4;&#x4E3A;&#x8FD9;&#x4E2A;&#x5730;&#x65B9;&#x662F;&#x79FB;&#x52A8;&#x7269;&#x4F53;&#x4E4B;&#x7C7B;&#x7684;&#x65E0;&#x6548;&#x533A;&#x57DF;&#x3002;</p>
</blockquote>
<h2 id="multi-view-stereo">Multi view stereo</h2>
<ul>
<li>MVDepthNet: Real-time Multiview Depth Estimation Neural Network <a href="https://github.com/HKUST-Aerial-Robotics/MVDepthNet" target="_blank">[code(pytorch)]</a><a href="https://arxiv.org/abs/1807.08563" target="_blank">[paper]</a></li>
</ul>
<blockquote>
<p>3DV2018&#xFF0C;&#x5927;&#x6982;&#x662F;&#x7B2C;&#x4E00;&#x7BC7;CNN&#x505A;mvs&#x7684;&#x8BBA;&#x6587;&#xFF0C;&#x57FA;&#x672C;&#x601D;&#x8DEF;&#x5C31;&#x662F;&#x628A;measure frame &#x5229;&#x7528;pose&#x548C;&#x591A;&#x4E2A;depth warp&#x5230;ref frame&#xFF0C;&#x627E;&#x5230;cost&#x6700;&#x5C0F;&#x7684;depth&#xFF0C;&#x627E;&#x6700;&#x5C0F;&#x7684;&#x8FC7;&#x7A0B;&#x7528;&#x4E00;&#x4E2A;unet&#x5B9E;&#x73B0;&#xFF0C;&#x591A;&#x4E2A;multi view&#x65F6;&#xFF0C;&#x5BF9;cost volume&#x6C42;&#x5E73;&#x5747;&#x3002;</p>
</blockquote>
<ul>
<li>MVSNet: Depth Inference for Unstructured Multi-view Stereo <a href="https://github.com/YoYo000/MVSNet" target="_blank">code(tensorflow)</a><a href="https://arxiv.org/abs/1804.02505" target="_blank">paper</a></li>
</ul>
<blockquote>
<p>ECCV2018&#xFF0C;&#x5728;MVDepthNet&#x57FA;&#x7840;&#x4E0A;&#x6DFB;&#x52A0;&#x4E86;&#x5BF9;&#x56FE;&#x50CF;&#x7684;feature&#x63D0;&#x53D6;&#xFF0C;&#x5E76;&#x5BF9;encode-decode&#x51FA;&#x6765;&#x7684;depth&#x52A0;&#x4E86;&#x56FE;&#x50CF;&#x4E0A;&#x7684;refine</p>
</blockquote>
<ul>
<li>DPSNet: End-to-end Deep Plane Sweep Stereo <a href="https://github.com/sunghoonim/DPSNet" target="_blank">[code(pytorch)]</a> <a href="https://arxiv.org/abs/1905.00538" target="_blank">[paper]</a></li>
</ul>
<blockquote>
<p>ICLR2019&#xFF0C;&#x601D;&#x8DEF;&#x548C;mvsnet&#x57FA;&#x672C;&#x4E00;&#x81F4;&#xFF0C;&#x540C;&#x65F6;&#x53D1;&#x8868;&#xFF0C;&#x4F46;&#x662F;&#x4EE3;&#x7801;&#x662F;pytorch&#x7684;&#xFF0C;&#x770B;&#x5F97;&#x51FA;&#x662F;&#x5728;MVDepthNet&#x4E0A;&#x6539;&#x7684;&#xFF0C;&#x4EE3;&#x7801;&#x8D28;&#x91CF;&#x6BD4;&#x8F83;&#x9AD8;</p>
</blockquote>
<ul>
<li>Depth Estimation by Learning Triangulation and Densification of Sparse Points for Multi-view Stereo</li>
</ul>
<p>arxiv 2020.5&#x6708;&#x7684;&#x4E00;&#x7BC7;&#xFF0C;&#x7528;superpoint&#x63D0;&#x63CF;&#x8FF0;&#x5B50;&#xFF0C;&#x505A;point match&#xFF0C;&#x5229;&#x7528;&#x7A00;&#x758F;&#x7684;&#x5339;&#x914D;&#x70B9;&#x548C;&#x4E00;&#x4E9B;&#x968F;&#x673A;&#x70B9;&#x505A;&#x4E09;&#x89D2;&#x6D4B;&#x91CF;&#xFF08;&#x4E5F;&#x53EF;&#x4EE5;&#x7406;&#x89E3;&#x4E3A;&#x7A00;&#x758F;&#x7684;cost volume&#xFF09;&#xFF0C;&#x7136;&#x540E;&#x7528;unet encode-decode&#x628A;&#x7A00;&#x758F;&#x7684;&#x6DF1;&#x5EA6;&#x56FE;&#x53D8;&#x6210;dense&#x7684;&#xFF0C;&#x76F8;&#x540C;&#x6548;&#x679C;&#x4E0B;&#xFF0C;&#x8BA1;&#x7B97;&#x91CF;&#x5C0F;&#x4E8E;cost volume&#x7684;&#x65B9;&#x6CD5;&#xFF0C;&#x4E0D;&#x8FC7;&#x53EA;&#x505A;&#x4E86;&#x5BA4;&#x5185;&#x7684;&#x5B9E;&#x9A8C;&#x3002;</p>
<ul>
<li>Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement <a href="https://github.com/svip-lab/FastMVSNet" target="_blank">[code(pytorch)]</a> <a href="https://arxiv.org/abs/2003.13017" target="_blank">[paper]</a></li>
</ul>
<blockquote>
<p>CVPR2020&#xFF0C;&#x7279;&#x5F81;&#x63D0;&#x53D6;&#x5C42;&#x5929;&#x7136;&#x4F1A;&#x7F29;&#x5C0F;feature map&#xFF0C;&#x5728;&#x5C0F;&#x7684;feature map&#x4E0A;&#x505A;plane sweep&#xFF0C;&#x5F97;&#x5230;winner takes all depth&#xFF0C;nearest upsample&#x5F97;&#x5230;&#x5927;&#x5206;&#x8FA8;&#x7387;depth map&#xFF0C;&#x518D;&#x7528;&#x539F;&#x56FE;&#x8F93;&#x51FA;&#x4E00;&#x4E2A;kxk&#x7684;&#x5377;&#x79EF;&#x6838;&#xFF0C;&#x6839;&#x636E;&#x8FD9;&#x4E2A;&#x5377;&#x79EF;&#x6838;&#xFF0C;&#x7528;&#x5468;&#x56F4;&#x7684;&#x4FE1;&#x606F;&#x4E30;&#x5BCC;depth map&#xFF0C;&#x518D;&#x7528;warp loss refine depth map&#xFF0C;&#x5C11;&#x4E86;encode-decode&#x5C42;&#xFF0C;&#x8BA1;&#x7B97;&#x91CF;&#x5C11;&#x4E86;&#x5F88;&#x591A;&#xFF0C;plane sweep&#x90E8;&#x5206;&#x5728;&#x5C0F;&#x5206;&#x8FA8;&#x7387;&#x4E0A;&#x505A;&#x7684;&#xFF0C;&#x8BA1;&#x7B97;&#x91CF;&#x4E5F;&#x5C0F;&#x3002;</p>
</blockquote>
<ul>
<li>Cost Volume Pyramid Based Depth Inference for Multi-View Stereo <a href="https://github.com/JiayuYANG/CVP-MVSNet" target="_blank">[code(pytorch)]</a> <a href="https://arxiv.org/abs/1912.08329" target="_blank">[paper]</a></li>
</ul>
<blockquote>
<p>CVPR2020, &#x63D0;&#x53D6;feature pyrammid&#xFF0C;&#x73B0;&#x5728;&#x6700;&#x5C0F;scale&#x7684;feature&#x4E0A;&#xFF0C;&#x5BF9;&#x6240;&#x6709;depth range&#x505A;plane sweep&#xFF0C;&#x63A5;3d&#x5377;&#x79EF;&#x51FA;depth&#xFF0C;&#x518D;&#x5BF9;&#x66F4;&#x5927;scale&#x7684;feature&#xFF0C;&#x5728;&#x5DF2;&#x7ECF;&#x4F30;&#x8BA1;&#x51FA;&#x6765;&#x7684;depth&#x9644;&#x8FD1;&#x51E0;&#x4E2A;channel&#x505A;plane sweep&#xFF0C;&#x63A5;3d&#x5377;&#x79EF;&#x51FA;&#x539F;depth&#x7684;&#x6B8B;&#x5DEE;&#xFF0C;&#x52A0;&#x5728;&#x539F;depth&#x4E0A;&#x4F5C;&#x4E3A;refine&#xFF0C;&#x4ECE;&#x800C;&#x5F97;&#x5230;multi scale&#xFF0C;&#x4E14;&#x8BA1;&#x7B97;&#x91CF;&#x8FD8;&#x4E0D;&#x4F1A;&#x592A;&#x5927;&#xFF08;&#x56E0;&#x4E3A;&#x540E;&#x9762;&#x53EA;&#x5728;&#x5468;&#x56F4;&#x7684;depth&#x641C;&#x7D22;&#xFF09;&#x7684;cost volume&#x3002;</p>
</blockquote>
<ul>
<li>Upgrading Optical Flow to 3D Scene Flow Through Optical Expansion-Supplementary Material<a href="https://github.com/gengshan-y/expansion" target="_blank">[code(pytorch)]</a><a href="https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_Upgrading_Optical_Flow_to_3D_Scene_Flow_Through_Optical_Expansion_CVPR_2020_paper.pdf" target="_blank">[paper]</a></li>
</ul>
<blockquote>
<p>CVPR2020&#xFF0C;&#x63A8;&#x5BFC;&#x51FA;optical expansion(&#x7269;&#x4F53;&#x957F;&#x5EA6;&#x5728;&#x50CF;&#x7D20;&#x5750;&#x6807;&#x7CFB;&#x4E0A;&#x7684;&#x53D8;&#x5316;)&#x548C;motion in depth&#x7684;&#x53CD;&#x6BD4;&#x5173;&#x7CFB;&#xFF08;&#x53EA;&#x5728;&#x6CA1;&#x6709;&#x65CB;&#x8F6C;&#x53EA;&#x6709;&#x5E73;&#x79FB;&#x65F6;&#x6210;&#x7ACB;&#xFF09;&#xFF0C;&#x7528;&#x4E00;&#x4E2A;encoder-decoder&#x8F93;&#x51FA;&#x5149;&#x6D41;&#xFF0C;&#x901A;&#x8FC7;&#x4E00;&#x4E2A;local affine layer&#x5F97;&#x5230;&#x521D;&#x59CB;&#x7684;expansion&#xFF0C;&#x518D;&#x901A;&#x8FC7;&#x4E00;&#x4E2A;encoder-decoder&#x5F97;&#x5230;refine&#x7684;expansion&#xFF0C;&#x518D;&#x7528;&#x4E00;&#x4E2A;encoder-decoder&#x5F97;&#x5230;motion-in-depth&#xFF0C;&#x4E3A;&#x4E86;&#x5F97;&#x5230;&#x771F;&#x6B63;&#x7684;motion in depth&#xFF0C;&#x8FD8;&#x9700;&#x8981;&#x7528;&#x4E00;&#x4E2A;&#x5355;&#x76EE;&#x7F51;&#x7EDC;&#x51FA;frame1&#x7684;depth&#xFF0C;&#x518D;&#x7528;motion in depth&#x6362;&#x7B97;&#x51FA;frame2&#x7684;depth&#xFF0C;motion in depth&#x7684;&#x601D;&#x8DEF;&#x6BD4;&#x8F83;&#x65B0;&#x5947;&#xFF0C;&#x4F46;&#x5E76;&#x4E0D;&#x600E;&#x4E48;&#x5B9E;&#x7528;&#x3002;</p>
</blockquote>
<h2 id="&#x5176;&#x4ED6;depth&#x76F8;&#x5173;&#x7684;&#x8BBA;&#x6587;">&#x5176;&#x4ED6;Depth&#x76F8;&#x5173;&#x7684;&#x8BBA;&#x6587;</h2>
<ul>
<li>Depth Sensing Beyond LiDAR Range</li>
</ul>
<blockquote>
<p>cvpr2020&#xFF0C;&#x6784;&#x9020;&#x4E86;&#x4E00;&#x4E2A;&#x4E09;&#x76EE;&#x7CFB;&#x7EDF;&#xFF0C;&#x8BB2;&#x4E86;&#x600E;&#x4E48;&#x7528;&#x4E09;&#x76EE;&#x51FA;&#x6DF1;&#x5EA6;&#xFF0C;&#x58F0;&#x79F0;&#x89E3;&#x51B3;&#x8D85;&#x8FDC;&#x8DDD;&#x79BB;&#xFF08;&#x8D85;&#x8FC7;lidar&#x8303;&#x56F4;&#xFF09;&#x6DF1;&#x5EA6;&#x4F30;&#x8BA1;&#xFF0C;&#x4F46;&#x662F;&#x8BBA;&#x6587;&#x91CC;&#x770B;&#x4E0D;&#x51FA;&#x4E3A;&#x5565;&#x4E09;&#x76EE;&#x80FD;&#x89E3;&#x8FD9;&#x4E2A;&#x95EE;&#x9898;&#x3002;</p>
</blockquote>

                                
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